Azure Container Registry allows you to store images for all types of container deployments including DC/OS, Docker Swarm, Kubernetes, and Azure services such as App Service, Batch, Service Fabric, and others. Your DevOps team can manage the configuration of apps isolated from the configuration of the hosting environment.
More information about Azure Container Registry and pricing

Azure DevOps Project will do the rest of the deployment.

Of course Infrastructure as Code (IaC) is possible by ARM JSON Template.

Your Continuous integration and continuous deployment to Azure IoT Edge is deployed and active. Now you have your Azure Pipeline in place to continuously update your IoT Device App. From here you can go to Azure DevOps Project Homepage.

Via Agent phase you can see all the jobs of the deployment.

Azure DevOps Pipeline Release

here we have Azure DevOps Repos

Azure DevOps Services includes free unlimited private Git repos, so Azure Repos is easy to try out. Git is the most commonly used version control system today and is quickly becoming the standard for version control. Git is a distributed version control system, meaning that your local copy of code is a complete version control repository. These fully functional local repositories make it easy to work offline or remotely. You commit your work locally, and then sync your copy of the repository with the copy on the server.
Git in Azure Repos is standard Git. You can use the clients and tools of your choice, such as Git for Windows, Mac, partners’ Git services, and tools such as Visual Studio and Visual Studio Code.

All the Azure Resources for the IoT Edge Pipeline with Azure DevOps.

When you have your Azure DevOps Pipeline with IoT Edge devices running, you can monitor your pipeline with Analytics inside Azure DevOps.

Conclusion :

When you connect Microsoft Azure IoT Edge – HUB with your Internet of Things Devices and combine it with Microsoft Azure DevOps Team to develop your Azure IoT Pipeline, then you are in fully control of Continuous integration and continuous deployment to Azure IoT Edge. From here you can make your innovations and Intelligent Cloud & Edge with Artificial Intelligence and Machine Learning to your Devices. You will see that this combination will be Awesome for HealthCare, Smart Cities, Smart Buildings, Infrastructure, and the Tech Industry.

Azure DevOps for CI/CD

Azure DevOps Services is a cloud service for collaborating on code development. It provides an integrated set of features that you access through your web browser or IDE client. The features are included, as follows:

Git repositories for source control of your code

Build and release services to support continuous integration and delivery of your apps

Agile tools to support planning and tracking your work, code defects, and issues using Kanban and Scrum methods

The Azure DevOps ecosystem also provides support for adding extensions and integrating with other popular services, such as: Campfire, Slack, Trello, UserVoice, and more, and developing your own custom extensions.

Previously known as Team Foundation Server (TFS), Azure DevOps Server is a set of collaborative software development tools, hosted on-premises. Azure DevOps Server integrates with your existing IDE or editor, enabling your cross-functional team to work effectively on projects of all sizes.

Azure services and infrastructure-as-code (IaC) make control plane automation very achievable. Many enterprise IT groups dream of creating or unifying their disparate automation processes and supporting a common, enterprise-wide datacenter control plane in the cloud that is integrated with their existing or new DevOps workflows. Their development environments may use Jenkins, Azure DevOps Services (formerly Visual Studio Team Services), Visual Studio Team Foundation Server (TFS), Atlassian, or other services. The challenge is to automate beyond the CI/CD pipeline to the management and policy layers. From a planning and architecture standpoint, it can seem like an overwhelming program of interdependent systems and processes. This guide outlines a planning process that you can use for automated support of your cloud deployments and DevOps workflows beyond the CI/CD pipeline. The Azure platform provides services you can use, or you can choose to work with third-party or open source options. The process is based on real-world examples that we have deployed with enterprise customers on Azure.

This whitepaper was authored by Tim Ehlen. It was edited by Nanette Ray. It was reviewed by AzureCAT.

Azure Pipelines for your Open Source Projects

Damian speaks to Edward Thomson about how to get started with Azure Pipelines – right from GitHub. The deep integration and GitHub Marketplace app for Azure Pipelines makes it incredibly easy to build your projects no matter what language you’re using. You can even use the builds as part of your PR checks!

Today at Microsoft Connect(); we introduced Azure Databricks, an exciting new service in preview that brings together the best of the Apache Spark analytics platform and Azure cloud. As a close partnership between Databricks and Microsoft, Azure Databricks brings unique benefits not present in other cloud platforms. This blog post introduces the technology and new capabilities available for data scientists, data engineers, and business decision-makers using the power of Databricks on Azure.

Azure Databricks Preview

Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.

In Databricks, you can create two different types of resources:
Standard Clusters: Databricks’ standard clusters have lot of configuration options to customize and fine tune your Spark jobs. You can learn more about standard clusters below.
Serverless Pools (BETA): With serverless pools, Databricks’ auto-manages all the resources and you just need to provide the range of instances required for the pool. Serverless pools support only Python and SQL. Serverless pools also auto-configures the resources with right Spark configuration. Visit Serverless Pools to know more about them.